{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Source](https://blog.keras.io/building-autoencoders-in-keras.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "from keras.layers import Input, Dense\n",
    "from keras.models import Model\n",
    "from keras.datasets import mnist\n",
    "from keras import regularizers\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "encoding_dim = 32\n",
    "\n",
    "input_img = Input(shape=(784,))\n",
    "# add a Dense layer with a L1 activity regularizer\n",
    "encoded = Dense(encoding_dim, activation='relu',\n",
    "                activity_regularizer=regularizers.l1(10e-5))(input_img)\n",
    "decoded = Dense(784, activation='sigmoid')(encoded)\n",
    "\n",
    "autoencoder = Model(input_img, decoded)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# this model maps an input to its encoded representation\n",
    "encoder = Model(input_img, encoded)\n",
    "\n",
    "# create a placeholder for an encoded (32-dimensional) input\n",
    "encoded_input = Input(shape=(encoding_dim,))\n",
    "# retrieve the last layer of the autoencoder model\n",
    "decoder_layer = autoencoder.layers[-1]\n",
    "# create the decoder model\n",
    "decoder = Model(encoded_input, decoder_layer(encoded_input))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "autoencoder.compile(optimizer='adadelta', loss='binary_crossentropy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "(x_train, _), (x_test, _) = mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(60000, 784)\n",
      "(10000, 784)\n"
     ]
    }
   ],
   "source": [
    "x_train = x_train.astype('float32') / 255.\n",
    "x_test = x_test.astype('float32') / 255.\n",
    "x_train = x_train.reshape((len(x_train), np.prod(x_train.shape[1:])))\n",
    "x_test = x_test.reshape((len(x_test), np.prod(x_test.shape[1:])))\n",
    "print x_train.shape\n",
    "print x_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 60000 samples, validate on 10000 samples\n",
      "Epoch 1/100\n",
      "60000/60000 [==============================] - 3s - loss: 0.6843 - val_loss: 0.6607\n",
      "Epoch 2/100\n",
      "60000/60000 [==============================] - 3s - loss: 0.6440 - val_loss: 0.6272\n",
      "Epoch 3/100\n",
      "60000/60000 [==============================] - 4s - loss: 0.6111 - val_loss: 0.5952\n",
      "Epoch 4/100\n",
      "60000/60000 [==============================] - 3s - loss: 0.5802 - val_loss: 0.5655\n",
      "Epoch 5/100\n",
      "60000/60000 [==============================] - 3s - loss: 0.5519 - val_loss: 0.5386\n",
      "Epoch 6/100\n",
      "60000/60000 [==============================] - 3s - loss: 0.5264 - val_loss: 0.5146\n",
      "Epoch 7/100\n",
      "60000/60000 [==============================] - 3s - loss: 0.5036 - val_loss: 0.4932\n",
      "Epoch 8/100\n",
      "60000/60000 [==============================] - 3s - loss: 0.4835 - val_loss: 0.4744\n",
      "Epoch 9/100\n",
      "60000/60000 [==============================] - 2s - loss: 0.4658 - val_loss: 0.4577\n",
      "Epoch 10/100\n",
      "60000/60000 [==============================] - 3s - loss: 0.4501 - val_loss: 0.4430\n",
      "Epoch 11/100\n",
      "60000/60000 [==============================] - 3s - loss: 0.4362 - val_loss: 0.4299\n",
      "Epoch 12/100\n",
      "60000/60000 [==============================] - 3s - loss: 0.4239 - val_loss: 0.4183\n",
      "Epoch 13/100\n",
      "60000/60000 [==============================] - 3s - loss: 0.4129 - val_loss: 0.4079\n",
      "Epoch 14/100\n",
      "60000/60000 [==============================] - 3s - loss: 0.4030 - val_loss: 0.3986\n",
      "Epoch 15/100\n",
      "60000/60000 [==============================] - 3s - loss: 0.3942 - val_loss: 0.3902\n",
      "Epoch 16/100\n",
      "60000/60000 [==============================] - 3s - loss: 0.3862 - val_loss: 0.3826\n",
      "Epoch 17/100\n",
      "60000/60000 [==============================] - 3s - loss: 0.3790 - val_loss: 0.3758\n",
      "Epoch 18/100\n",
      "60000/60000 [==============================] - 3s - loss: 0.3725 - val_loss: 0.3696\n",
      "Epoch 19/100\n",
      "60000/60000 [==============================] - 3s - loss: 0.3665 - val_loss: 0.3639\n",
      "Epoch 20/100\n",
      "60000/60000 [==============================] - 3s - loss: 0.3611 - val_loss: 0.3587\n",
      "Epoch 21/100\n",
      "60000/60000 [==============================] - 3s - loss: 0.3561 - val_loss: 0.3539\n",
      "Epoch 22/100\n",
      "60000/60000 [==============================] - 3s - loss: 0.3516 - val_loss: 0.3496\n",
      "Epoch 23/100\n",
      "60000/60000 [==============================] - 3s - loss: 0.3474 - val_loss: 0.3455\n",
      "Epoch 24/100\n",
      "60000/60000 [==============================] - 3s - loss: 0.3435 - val_loss: 0.3418\n",
      "Epoch 25/100\n",
      "60000/60000 [==============================] - 3s - loss: 0.3399 - val_loss: 0.3384\n",
      "Epoch 26/100\n",
      "60000/60000 [==============================] - 3s - loss: 0.3366 - val_loss: 0.3352\n",
      "Epoch 27/100\n",
      "60000/60000 [==============================] - 3s - loss: 0.3335 - val_loss: 0.3322\n",
      "Epoch 28/100\n",
      "60000/60000 [==============================] - 3s - loss: 0.3307 - val_loss: 0.3295\n",
      "Epoch 29/100\n",
      "60000/60000 [==============================] - 3s - loss: 0.3280 - val_loss: 0.3269\n",
      "Epoch 30/100\n",
      "60000/60000 [==============================] - 3s - loss: 0.3255 - val_loss: 0.3245\n",
      "Epoch 31/100\n",
      "60000/60000 [==============================] - 3s - loss: 0.3232 - val_loss: 0.3222\n",
      "Epoch 32/100\n",
      "60000/60000 [==============================] - 3s - loss: 0.3210 - val_loss: 0.3201\n",
      "Epoch 33/100\n",
      "60000/60000 [==============================] - 3s - loss: 0.3189 - val_loss: 0.3181\n",
      "Epoch 34/100\n",
      "60000/60000 [==============================] - 3s - loss: 0.3170 - val_loss: 0.3162\n",
      "Epoch 35/100\n",
      "60000/60000 [==============================] - 3s - loss: 0.3152 - val_loss: 0.3145\n",
      "Epoch 36/100\n",
      "60000/60000 [==============================] - 3s - loss: 0.3135 - val_loss: 0.3128\n",
      "Epoch 37/100\n",
      "60000/60000 [==============================] - 3s - loss: 0.3119 - val_loss: 0.3112\n",
      "Epoch 38/100\n",
      "60000/60000 [==============================] - 3s - loss: 0.3103 - val_loss: 0.3097\n",
      "Epoch 39/100\n",
      "60000/60000 [==============================] - 3s - loss: 0.3089 - val_loss: 0.3083\n",
      "Epoch 40/100\n",
      "60000/60000 [==============================] - 3s - loss: 0.3075 - val_loss: 0.3070\n",
      "Epoch 41/100\n",
      "60000/60000 [==============================] - 3s - loss: 0.3062 - val_loss: 0.3057\n",
      "Epoch 42/100\n",
      "60000/60000 [==============================] - 3s - loss: 0.3050 - val_loss: 0.3045\n",
      "Epoch 43/100\n",
      "60000/60000 [==============================] - 3s - loss: 0.3038 - val_loss: 0.3034\n",
      "Epoch 44/100\n",
      "60000/60000 [==============================] - 3s - loss: 0.3027 - val_loss: 0.3023\n",
      "Epoch 45/100\n",
      "60000/60000 [==============================] - 3s - loss: 0.3016 - val_loss: 0.3012\n",
      "Epoch 46/100\n",
      "60000/60000 [==============================] - 3s - loss: 0.3006 - val_loss: 0.3002\n",
      "Epoch 47/100\n",
      "60000/60000 [==============================] - 2s - loss: 0.2997 - val_loss: 0.2993\n",
      "Epoch 48/100\n",
      "60000/60000 [==============================] - 2s - loss: 0.2987 - val_loss: 0.2984\n",
      "Epoch 49/100\n",
      "60000/60000 [==============================] - 3s - loss: 0.2979 - val_loss: 0.2975\n",
      "Epoch 50/100\n",
      "60000/60000 [==============================] - 3s - loss: 0.2970 - val_loss: 0.2967\n",
      "Epoch 51/100\n",
      "60000/60000 [==============================] - 3s - loss: 0.2962 - val_loss: 0.2959\n",
      "Epoch 52/100\n",
      "60000/60000 [==============================] - 3s - loss: 0.2954 - val_loss: 0.2951\n",
      "Epoch 53/100\n",
      "60000/60000 [==============================] - 3s - loss: 0.2947 - val_loss: 0.2944\n",
      "Epoch 54/100\n",
      "60000/60000 [==============================] - 3s - loss: 0.2940 - val_loss: 0.2937\n",
      "Epoch 55/100\n",
      "60000/60000 [==============================] - 3s - loss: 0.2933 - val_loss: 0.2930\n",
      "Epoch 56/100\n",
      "60000/60000 [==============================] - 3s - loss: 0.2926 - val_loss: 0.2924\n",
      "Epoch 57/100\n",
      "60000/60000 [==============================] - 3s - loss: 0.2920 - val_loss: 0.2917\n",
      "Epoch 58/100\n",
      "60000/60000 [==============================] - 3s - loss: 0.2914 - val_loss: 0.2911\n",
      "Epoch 59/100\n",
      "60000/60000 [==============================] - 3s - loss: 0.2908 - val_loss: 0.2906\n",
      "Epoch 60/100\n",
      "60000/60000 [==============================] - 3s - loss: 0.2902 - val_loss: 0.2900\n",
      "Epoch 61/100\n",
      "60000/60000 [==============================] - 3s - loss: 0.2897 - val_loss: 0.2895\n",
      "Epoch 62/100\n",
      "60000/60000 [==============================] - 3s - loss: 0.2892 - val_loss: 0.2889\n",
      "Epoch 63/100\n",
      "60000/60000 [==============================] - 3s - loss: 0.2886 - val_loss: 0.2884\n",
      "Epoch 64/100\n",
      "60000/60000 [==============================] - 3s - loss: 0.2882 - val_loss: 0.2880\n",
      "Epoch 65/100\n",
      "60000/60000 [==============================] - 3s - loss: 0.2877 - val_loss: 0.2875\n",
      "Epoch 66/100\n",
      "60000/60000 [==============================] - 3s - loss: 0.2872 - val_loss: 0.2870\n",
      "Epoch 67/100\n",
      "60000/60000 [==============================] - 3s - loss: 0.2868 - val_loss: 0.2866\n",
      "Epoch 68/100\n",
      "60000/60000 [==============================] - 3s - loss: 0.2864 - val_loss: 0.2862\n",
      "Epoch 69/100\n",
      "60000/60000 [==============================] - 3s - loss: 0.2859 - val_loss: 0.2858\n",
      "Epoch 70/100\n",
      "60000/60000 [==============================] - 3s - loss: 0.2855 - val_loss: 0.2854\n",
      "Epoch 71/100\n",
      "60000/60000 [==============================] - 3s - loss: 0.2851 - val_loss: 0.2850\n",
      "Epoch 72/100\n",
      "60000/60000 [==============================] - 3s - loss: 0.2848 - val_loss: 0.2846\n",
      "Epoch 73/100\n",
      "60000/60000 [==============================] - 3s - loss: 0.2844 - val_loss: 0.2842\n",
      "Epoch 74/100\n",
      "60000/60000 [==============================] - 3s - loss: 0.2840 - val_loss: 0.2839\n",
      "Epoch 75/100\n",
      "60000/60000 [==============================] - 3s - loss: 0.2837 - val_loss: 0.2835\n",
      "Epoch 76/100\n",
      "60000/60000 [==============================] - 3s - loss: 0.2834 - val_loss: 0.2832\n",
      "Epoch 77/100\n",
      "60000/60000 [==============================] - 4s - loss: 0.2830 - val_loss: 0.2829\n",
      "Epoch 78/100\n",
      "60000/60000 [==============================] - 4s - loss: 0.2827 - val_loss: 0.2826\n",
      "Epoch 79/100\n",
      "60000/60000 [==============================] - 4s - loss: 0.2824 - val_loss: 0.2823\n",
      "Epoch 80/100\n",
      "60000/60000 [==============================] - 4s - loss: 0.2821 - val_loss: 0.2820\n",
      "Epoch 81/100\n",
      "60000/60000 [==============================] - 4s - loss: 0.2818 - val_loss: 0.2817\n",
      "Epoch 82/100\n",
      "60000/60000 [==============================] - 4s - loss: 0.2815 - val_loss: 0.2814\n",
      "Epoch 83/100\n",
      "60000/60000 [==============================] - 4s - loss: 0.2813 - val_loss: 0.2811\n",
      "Epoch 84/100\n",
      "60000/60000 [==============================] - 4s - loss: 0.2810 - val_loss: 0.2808\n",
      "Epoch 85/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "60000/60000 [==============================] - 3s - loss: 0.2807 - val_loss: 0.2806\n",
      "Epoch 86/100\n",
      "60000/60000 [==============================] - 3s - loss: 0.2805 - val_loss: 0.2803\n",
      "Epoch 87/100\n",
      "60000/60000 [==============================] - 3s - loss: 0.2802 - val_loss: 0.2801\n",
      "Epoch 88/100\n",
      "60000/60000 [==============================] - 5s - loss: 0.2800 - val_loss: 0.2798\n",
      "Epoch 89/100\n",
      "60000/60000 [==============================] - 4s - loss: 0.2798 - val_loss: 0.2796\n",
      "Epoch 90/100\n",
      "60000/60000 [==============================] - 5s - loss: 0.2795 - val_loss: 0.2794\n",
      "Epoch 91/100\n",
      "60000/60000 [==============================] - 5s - loss: 0.2793 - val_loss: 0.2792\n",
      "Epoch 92/100\n",
      "60000/60000 [==============================] - 4s - loss: 0.2791 - val_loss: 0.2789\n",
      "Epoch 93/100\n",
      "60000/60000 [==============================] - 4s - loss: 0.2789 - val_loss: 0.2787\n",
      "Epoch 94/100\n",
      "60000/60000 [==============================] - 4s - loss: 0.2787 - val_loss: 0.2785\n",
      "Epoch 95/100\n",
      "60000/60000 [==============================] - 4s - loss: 0.2785 - val_loss: 0.2783\n",
      "Epoch 96/100\n",
      "60000/60000 [==============================] - 4s - loss: 0.2783 - val_loss: 0.2781\n",
      "Epoch 97/100\n",
      "60000/60000 [==============================] - 4s - loss: 0.2781 - val_loss: 0.2779\n",
      "Epoch 98/100\n",
      "60000/60000 [==============================] - 4s - loss: 0.2779 - val_loss: 0.2777\n",
      "Epoch 99/100\n",
      "60000/60000 [==============================] - 4s - loss: 0.2777 - val_loss: 0.2775\n",
      "Epoch 100/100\n",
      "60000/60000 [==============================] - 4s - loss: 0.2775 - val_loss: 0.2774\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7f794f086390>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "autoencoder.fit(x_train, x_train,\n",
    "                epochs=100,\n",
    "                batch_size=256,\n",
    "                shuffle=True,\n",
    "                validation_data=(x_test, x_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# encode and decode some digits\n",
    "# note that we take them from the *test* set\n",
    "encoded_imgs = encoder.predict(x_test)\n",
    "decoded_imgs = decoder.predict(encoded_imgs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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sTA9Qv9+XJIPX3C16qaBXVS6XQz6fRzgcljE5diPYEZlOp6/+MPkUYLeQ0u7D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\nIYQQQiICgxpCCCGEEEIIIYSQiPD/AF8eIUV1yPH4AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f790c1af910>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# use Matplotlib (don't ask)\n",
    "\n",
    "n = 10  # how many digits we will display\n",
    "plt.figure(figsize=(20, 4))\n",
    "for i in range(n):\n",
    "    # display original\n",
    "    ax = plt.subplot(2, n, i + 1)\n",
    "    plt.imshow(x_test[i].reshape(28, 28))\n",
    "    plt.gray()\n",
    "    ax.get_xaxis().set_visible(False)\n",
    "    ax.get_yaxis().set_visible(False)\n",
    "\n",
    "    # display reconstruction\n",
    "    ax = plt.subplot(2, n, i + 1 + n)\n",
    "    plt.imshow(decoded_imgs[i].reshape(28, 28))\n",
    "    plt.gray()\n",
    "    ax.get_xaxis().set_visible(False)\n",
    "    ax.get_yaxis().set_visible(False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
